The Blueprint: Whitepapers by SIMCEL
Strategic thinking for the next generation of planning
Explore SIMCEL’s foundational thinking designed to reimagine planning performance.
From system design to behavioral insights and simulation intelligence, each whitepaper offers a clear, structured path to smarter enterprise decisions.
The Third-Era IBP
A bold reframing of Integrated Business Planning, this strategic paper introduces the "IBP 3.0" effectiveness framework, guiding manufacturing enterprises beyond legacy S&OP processes. By eliminating data-to-decision breaks and integrating financial and ESG simulation, it enables true ROCE-driven planning across demand, supply, and finance functions.
Julien Brun
#Integrated Business Planning, #IBP 3.0, #ROCE, #C-Suite, #Strategy
The Lab: Technical Papers by SIMCEL
Experiments, models, and insights shaping smarter planning
Step inside SIMCEL’s research environment—where we test, validate, and challenge the mechanics of modern planning.
From forecast accuracy and price elasticity to inventory logic and behavioral dynamics, these technical papers reveal the methods and findings behind better decisions and system performance.
Dynamic Pricing
Dynamic Pricing is a revenue optimization strategy where product or service prices are adjusted in real time based on market demand, competition, and contextual signals. This approach uses data-driven algorithms—such as Bayesian models, reinforcement learning, and decision trees—to tailor prices to consumer behavior, seasonal patterns, supply conditions, and competitor actions.
Widely used in industries like eCommerce, airlines, hospitality, and event management, dynamic pricing transforms pricing from a static decision into a flexible growth lever. When implemented correctly, it increases profitability, balances supply-demand dynamics, and enhances market responsiveness while maintaining fairness and transparency.
#PricingStrategy, #PriceOptimization, #RevenueManagement, #RealTimePricing
Forecast Technique
Forecasting lies at the core of Integrated Business Planning, yet too often, organizations rely on static, one-size-fits-all models that fail to reflect real-world dynamics. This paper explores SIMCEL’s approach to forecasting, combining statistical methods, machine learning, and behavior-informed heuristics to model demand with greater precision and adaptability.
We examine the trade-offs between simplicity and sophistication, the role of data granularity, and the calibration of forecast strategies across product lifecycles, market volatility, and business objectives. Practical experiments illustrate how hybrid models outperform traditional baselines in accuracy, stability, and decision utility.
The result: a more transparent, contextual, and controllable forecasting layer that unlocks better planning and higher confidence across the enterprise.